From Marks to Maturity: Should Degrees Assess How Learners Develop?
Why a degree measures what a student produced at particular moments, and what it would take to measure how the student changed
Education and Research
Pedagogy, learning design, and research methods.
GenAI in Higher Education
Beyond Marks: A Developmental Model of Higher Education
If development were the main thing a degree assessed, what would the degree look like, and would it be better?
Students Have Always Been Able to Cheat
Why generative AI changes the form of academic misconduct without creating the problem, and what that means for assessment
From Binary to Agents: Abstraction, Automation and the Future of Programming
Why each generation of programmers has been told that the new layer isn't "real programming", and what that tells us about the layer we are in now
How I Use AI in Teaching, and How Students Use It Responsibly
Principles, practice, and the responsibility students carry for their own work
Academic Misconduct and GenAI
Generative AI tools reached students faster than institutions could write policy for them. Within months of ChatGPT's public release, UK universities had to decide, case by case and often under press scrutiny, whether…
What Higher Education Needs to Do About GenAI
Most of the public debate about generative AI in universities has focused on catching misconduct. That is necessary but not sufficient — the harder, less visible problem is that GenAI is now something most students use…
Avoiding the AI Short-Circuit
The strongest evidence currently available on generative AI and learning outcomes — a 30-month panel study of 26,811 secondary students — finds that using GenAI to complete homework raises assignment scores by 18% and…
Supporting Students with GenAI: Why Trust Comes Before Policy
Most guidance on students and generative AI is a list of rules, and rules only reach students who are already listening. This page starts from a different observation, made by one lecturer over several cohorts: students…
Teaching with GenAI: Require It, Then Ask What It Did
A ban on AI in assessed work is hard to sustain when students are already using it. In the HEPI/Kortext survey, 92% of undergraduates used generative AI and 88% had used it on assessed work [1] . That leaves two…
Local AI for Marking Support: A Live Build, and Why It Isn't Ready Yet
This page describes a real, working piece of software, not a description of a plan: a pipeline that runs locally hosted language models over substantial student project reports and produces per-criterion marks…
Generative AI in the Curriculum: Delegation, Collaboration, and Educational Purpose
Asking what an activity is for before deciding what role AI should play in it
Synthesis by humans and machines
Generative AI has made it easy to produce something that looks like an original piece of work — an essay, a design, a working program — without a person having done the combining themselves. That's forced a useful…
Educational Practice and Support
Managing Individual Learning Plans Across Large Cohorts
An Individual Learning Plan (ILP) — the record of the reasonable adjustments a disabled student needs, whatever a particular institution's paperwork calls it — is easy to manage well for a handful of students and hard…
Rubrics in HE
Detailed rubrics carry a real, moderate, well-documented average benefit to the marks awarded on assessed work and to self-regulated learning — but that outcome measure is not the same thing as learning retained, and…
Learning Theory and Philosophy
Learning as Connection, Contradiction, and Self-Construction (series hub)
Knowledge networks, metacognition, productive struggle, narrative identity, and the ethics of curriculum
Learning as a Network: Connectivism, Knowledge and Conceptual Navigation
Connections among people, artefacts, sources, and systems, and what a learner has to do with them
Contradiction and the Productive Struggle of Learning
When the learner's model meets a world that behaves differently
The Self-Model as a Learning Tool: Metacognition, Regulation and Agency
Part 2 of Learning as Connection, Contradiction, and Self-Construction
Learning Logs and Narrative Identity: Evidence, Memory and the Construction of the Learner
Why a record of activity matters more than the story told about it afterwards
Should This Be Taught? An Ethical and Political Evaluation of Curriculum
Applying the V3 model to curriculum, difficulty, and educational consequence
Pedagogy for Networked and Agentic Learning
Connection, activity, evidence, and revision as design principles for teaching
AI-Augmented Learning Systems
The Caring Learning Agent: Open Learner Models, Metacognition and AI Assistance
Making the system's model of the learner visible, negotiable, and accountable
Networked Learning Assistants: Permissions, Sharing and Personal Learning Networks
Scoped sharing of learner models, and the learner's own network of people, sources, and tools
Renting or Owning: Cloud Dependence, Retention, and Capability
What a change to a free service can reveal about where an organisation's data and abilities actually sit
Research Methods
Grounded Theory at AI Speed: Borrowing Agile Patterns for a Domain That Won't Hold Still
Grounded theory was built for domains that sit still long enough to be understood: gather data, code it, gather more to test the emerging categories against, and keep cycling until new data stops producing new…
Research Case Studies
Fractal Terrain Report
A Digital Elevation Model (DEM) is a raster grid that assigns an elevation value to each cell of a geographic area, and it underpins most terrain work in GIS, hydrology, disaster planning and computer graphics alike [1]…